






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


SCREAMING_SNAKE_CASE__   :      str				=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      str				=		{
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	'''dpt'''







				def __init__(    self							,       __lowerCAmelCase=7_6_8							,       __lowerCAmelCase=1_2							,       __lowerCAmelCase=1_2							,       __lowerCAmelCase=3_0_7_2							,       __lowerCAmelCase="gelu"							,       __lowerCAmelCase=0.0							,       __lowerCAmelCase=0.0							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=1E-12							,       __lowerCAmelCase=3_8_4							,       __lowerCAmelCase=1_6							,       __lowerCAmelCase=3							,       __lowerCAmelCase=False							,       __lowerCAmelCase=True							,       __lowerCAmelCase=[2, 5, 8, 1_1]							,       __lowerCAmelCase="project"							,       __lowerCAmelCase=[4, 2, 1, 0.5]							,       __lowerCAmelCase=[9_6, 1_9_2, 3_8_4, 7_6_8]							,       __lowerCAmelCase=2_5_6							,       __lowerCAmelCase=-1							,       __lowerCAmelCase=False							,       __lowerCAmelCase=True							,       __lowerCAmelCase=0.4							,       __lowerCAmelCase=2_5_5							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=[1, 1_0_2_4, 2_4, 2_4]							,       __lowerCAmelCase=[0, 1]							,       __lowerCAmelCase=None							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase  )

								__magic_name__		:str   =   hidden_size
								__magic_name__		:Union[str, Any]   =   is_hybrid

								if self.is_hybrid:
												if backbone_config is None:
																logger.info('''Initializing the config with a `BiT` backbone.'''  )
																__magic_name__		:Tuple   =   {
																    '''global_padding''': '''same''',
																    '''layer_type''': '''bottleneck''',
																    '''depths''': [3, 4, 9],
																    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
																    '''embedding_dynamic_padding''': True,
																}
																__magic_name__		:List[Any]   =   BitConfig(**__lowerCAmelCase  )
												elif isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ):
																logger.info('''Initializing the config with a `BiT` backbone.'''  )
																__magic_name__		:Tuple   =   BitConfig(**__lowerCAmelCase  )
												elif isinstance(__lowerCAmelCase							,       __lowerCAmelCase  ):
																__magic_name__		:Dict   =   backbone_config
												else:
																raise ValueError(
																    F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''  )

												__magic_name__		:Any   =   backbone_featmap_shape
												__magic_name__		:Any   =   neck_ignore_stages

												if readout_type != "project":
																raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''  )
								else:
												__magic_name__		:Dict   =   None
												__magic_name__		:Dict   =   None
												__magic_name__		:List[Any]   =   []

								__magic_name__		:int   =   num_hidden_layers
								__magic_name__		:Union[str, Any]   =   num_attention_heads
								__magic_name__		:Tuple   =   intermediate_size
								__magic_name__		:List[Any]   =   hidden_act
								__magic_name__		:int   =   hidden_dropout_prob
								__magic_name__		:Dict   =   attention_probs_dropout_prob
								__magic_name__		:int   =   initializer_range
								__magic_name__		:Tuple   =   layer_norm_eps
								__magic_name__		:List[Any]   =   image_size
								__magic_name__		:str   =   patch_size
								__magic_name__		:str   =   num_channels
								__magic_name__		:Union[str, Any]   =   qkv_bias
								__magic_name__		:Any   =   backbone_out_indices
								if readout_type not in ["ignore", "add", "project"]:
												raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''  )
								__magic_name__		:Tuple   =   readout_type
								__magic_name__		:List[Any]   =   reassemble_factors
								__magic_name__		:Any   =   neck_hidden_sizes
								__magic_name__		:int   =   fusion_hidden_size
								__magic_name__		:str   =   head_in_index
								__magic_name__		:Any   =   use_batch_norm_in_fusion_residual
								# auxiliary head attributes (semantic segmentation)
								__magic_name__		:Dict   =   use_auxiliary_head
								__magic_name__		:Dict   =   auxiliary_loss_weight
								__magic_name__		:Optional[int]   =   semantic_loss_ignore_index
								__magic_name__		:Any   =   semantic_classifier_dropout







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   copy.deepcopy(self.__dict__  )

								if output["backbone_config"] is not None:
												__magic_name__		:List[str]   =   self.backbone_config.to_dict()

								__magic_name__		:Optional[Any]   =   self.__class__.model_type
								return output














'''simple docstring'''







import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = False   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(
             UpperCamelCase__   ,	split=UpperCamelCase__   ,	features=UpperCamelCase__   ,	cache_dir=UpperCamelCase__   ,	keep_in_memory=UpperCamelCase__   ,	streaming=UpperCamelCase__   ,	num_proc=UpperCamelCase__   ,	**UpperCamelCase__   ,	)
         a_						     =			field
         a_						     =			path_or_paths if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else {self.split: path_or_paths}
         a_						     =			Json(
             cache_dir=UpperCamelCase__   ,	data_files=UpperCamelCase__   ,	features=UpperCamelCase__   ,	field=UpperCamelCase__   ,	**UpperCamelCase__   ,	)







  def    _a       (			self	):






         """simple docstring"""


         if self.streaming:
                a_						     =			self.builder.as_streaming_dataset(split=self.split	)
         # Build regular (map-style) dataset
         else:
                a_						     =			None
                a_						     =			None
                a_						     =			None
                a_						     =			None

                self.builder.download_and_prepare(
                    download_config=UpperCamelCase__   ,	download_mode=UpperCamelCase__   ,	verification_mode=UpperCamelCase__   ,	base_path=UpperCamelCase__   ,	num_proc=self.num_proc   ,	)
                a_						     =			self.builder.as_dataset(
                    split=self.split   ,	verification_mode=UpperCamelCase__   ,	in_memory=self.keep_in_memory	)
         return dataset





class   __SCREAMING_SNAKE_CASE       :


  """simple docstring"""


  def __init__(			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__ = None   ,	UpperCamelCase__ = None   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         if num_proc is not None and num_proc <= 0:
                raise ValueError(f'num_proc {num_proc} must be an integer > 0.'	)

         a_						     =			dataset
         a_						     =			path_or_buf
         a_						     =			batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         a_						     =			num_proc
         a_						     =			'utf-8'
         a_						     =			to_json_kwargs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.to_json_kwargs.pop('path_or_buf'   ,	UpperCamelCase__	)
         a_						     =			self.to_json_kwargs.pop('orient'   ,	'records'	)
         a_						     =			self.to_json_kwargs.pop('lines'   ,	True if orient == 'records' else False	)
         a_						     =			self.to_json_kwargs.pop('index'   ,	False if orient in ['split', 'table'] else True	)
         a_						     =			self.to_json_kwargs.pop('compression'   ,	UpperCamelCase__	)

         if compression not in [None, "infer", "gzip", "bz2", "xz"]:
                raise NotImplementedError(f'`datasets` currently does not support {compression} compression'	)

         if isinstance(self.path_or_buf   ,	(str, bytes, os.PathLike)	):
                with fsspec.open(self.path_or_buf   ,	'wb'   ,	compression=UpperCamelCase__	) as buffer:
                       a_						     =			self._write(file_obj=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         else:
                if compression:
                       raise NotImplementedError(
                           f'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                           ' was passed. Please provide a local path instead.'	)
                a_						     =			self._write(
                    file_obj=self.path_or_buf   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**self.to_json_kwargs	)
         return written







  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_     ,	a_     ,	a_     ,	a_     ,	a_						     =			args

         a_						     =			query_table(
             table=self.dataset.data   ,	key=slice(UpperCamelCase__   ,	offset + self.batch_size	)   ,	indices=self.dataset._indices   ,	)
         a_						     =			batch.to_pandas().to_json(
             path_or_buf=UpperCamelCase__   ,	orient=UpperCamelCase__   ,	lines=UpperCamelCase__   ,	index=UpperCamelCase__   ,	**UpperCamelCase__	)
         if not json_str.endswith('\n'	):
                json_str += "\n"
         return json_str.encode(self.encoding	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	UpperCamelCase__   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         a_						     =			0

         if self.num_proc is None or self.num_proc == 1:
                for offset in logging.tqdm(
                    range(0   ,	len(self.dataset	)   ,	self.batch_size	)   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                       a_						     =			self._batch_json((offset, orient, lines, index, to_json_kwargs)	)
                       written += file_obj.write(UpperCamelCase__	)
         else:
                a_     ,	a_						     =			len(self.dataset	), self.batch_size
                with multiprocessing.Pool(self.num_proc	) as pool:
                       for json_str in logging.tqdm(
                           pool.imap(
                               self._batch_json   ,	[(offset, orient, lines, index, to_json_kwargs) for offset in range(0   ,	UpperCamelCase__   ,	UpperCamelCase__	)]   ,	)   ,	total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size   ,	unit='ba'   ,	disable=not logging.is_progress_bar_enabled()   ,	desc='Creating json from Arrow format'   ,	):
                              written += file_obj.write(UpperCamelCase__	)

         return written




